Samsung open-sources LittleBit: extreme quantization fits a 13B model in under 1GB
udmrzn · x · 2026-10-10
Samsung published and open-sourced LittleBit, an extreme quantization method that shrinks a 13B-parameter LLM into less than 1GB of memory.
The approach: instead of storing weights as standard numbers, latent factorization crushes them to sub-1-bit levels — as low as 0.1 bit per weight in some configurations. The architectural twist: at this compression level the hardware can "stop doing math" — LittleBit replaces core floating-point multiplication with a bitwise XOR operation, swapping matrix math for sign flips.
Results include up to 11.6x inference speedup. One analogy circulating: it's like running a Kimi-K3-class model on a single DGX Spark. If it reaches production, local on-device AI deployment could change for good.
Related event: Samsung Open-Sources LittleBit: 13B LLM Compressed Under 1GB(4 posts)→
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